CAMPBELL, CA, USA, May 24, 2018 – Building on its strategy to develop innovations that drive retail transformation for brands, retailers and manufacturers, PLM leader Centric Software announces the unveiling of its first artificial intelligence-based PLM module. Centric Software provides the most innovative enterprise solutions to fashion, retail, footwear, outdoor, luxury and consumer goods companies to achieve strategic and operational digital transformation goals. The term'artificial intelligence' dates back to the 1950's when computer scientist John McCarthy coined the phrase to describe the potential'thinking machines' of the future. Today, artificial intelligence (AI) tools are systems modeled on the problem-solving abilities of the human brain, breaking complex problems down into different layers of information comprised of many smaller problems. Applications vary considerably ranging from virtual assistants like'Alexa' and'Siri' to Netflix viewing recommendations to Amazon recommending things we might like to buy.

Training large-scale image recognition models is computationally expensive. This raises the question of whether there might be simple ways to improve the test performance of an already trained model without having to re-train or even fine-tune it with new data. Here, we show that, surprisingly, this is indeed possible. The key observation we make is that the layers of a deep network close to the output layer contain independent, easily extractable class-relevant information that is not contained in the output layer itself. We propose to extract this extra class-relevant information using a simple key-value cache memory to improve the classification performance of the model at test time. Our cache memory is directly inspired by a similar cache model previously proposed for language modeling (Grave et al., 2017). This cache component does not require any training or fine-tuning; it can be applied to any pre-trained model and, by properly setting only two hyper-parameters, leads to significant improvements in its classification performance. Improvements are observed across several architectures and datasets. In the cache component, using features extracted from layers close to the output (but not from the output layer itself) as keys leads to the largest improvements. Concatenating features from multiple layers to form keys can further improve performance over using single-layer features as keys. The cache component also has a regularizing effect, a simple consequence of which is that it substantially increases the robustness of models against adversarial attacks.

The application uses machine learning and, by extension, artificial intelligence, to properly identify people's faces and surface relevant factoids. Users can access Who's Who either through Sky News's mobile app or its website. The idea is to provide digital onlookers second-screen content to fill in gaps during the event. The app will, in real time, identify the faces of people at the wedding. According to a press release, the app uses Amazon Rekognition tools to name people in the crowd and then surface biographical information about them. As Sky News explains it, Who's Who will "[name] wedding guests as they arrive for the ceremony and tells people about their connection to the royal couple."

Despite the advancement of supervised image recognition algorithms, their de- pendence on the availability of labeled data and the rapid expansion of image categories raise the significant challenge of zero-shot learning. Zero-shot learn- ing (ZSL) aims to transfer knowledge from labeled classes into unlabeled classes to reduce human labeling effort. In this paper, we propose a novel self-training ensemble network model to address zero-shot image recognition. The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, each of which facilitates information transfer to different subsets of unlabeled classes. A self-training framework is then deployed to iteratively label the most confident images in each unlabeled class with predicted pseudo-labels and update the ensem- ble network with the training data augmented by the pseudo-labels. The proposed model performs training on both labeled and unlabeled data. It can naturally bridge the domain shift problem in visual appearances and be extended to the generalized zero-shot learning scenario. We conduct experiments on multiple standard ZSL datasets and the empirical results demonstrate the efficacy of the proposed model.

SikuliX is very unusual – a scripting/automation technology that relies on pattern matching, and is available for use via Python or Java. Developed at the User Interface Design Group at MIT, is a powerful and easy-to-use technology that uses image recognition to automate just about anything that appears on-screen. Sikuli is rather hard to slot – it offers all of the functionality of an automation or scripting tool, but it also offers some powerful and very novel image-matching functionality for truly novel use-cases that revolve around image search. In addition it has an OCR-mode, in which image matches are performed after converting those image patterns to text. This gives rise to some pretty new applications.

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During the opening F8 2018 keynote, Facebook CEO Mark Zuckerberg showed off the company's latest Instagram updates: Spotify integration, AI-based anti-bullying comment filters, AR camera effects and four-way video chat. During the Day 2 keynote, Facebook revealed how your daily Instagram updates are giving its AI technology a deep-learning crash course in image recognition--one that's apparently made its AI even smarter than Google's at categorizing objects in photos. Facebook pulled this off, amazingly enough, by instructing its AI to read photo hashtags and interpret photos' subject matter. Using this strategy, called "weakly supervised training", Facebook's AI achieved a record 85.4% accuracy rating on an industry-wide test of image recognition, beating out Google's previous record. A Facebook Engineering blog post went into detail on the methods.

In the race to continue building more sophisticated AI deep learning models, Facebook has a secret weapon: billions of images on Instagram. In research the company is presenting today at F8, Facebook details how it took what amounted to billions of public Instagram photos that had been annotated by users with hashtags and used that data to train their own image recognition models. They relied on hundreds of GPUs running around the clock to parse the data, but were ultimately left with deep learning models that beat industry benchmarks, the best of which achieved 85.4 percent accuracy on ImageNet. If you've ever put a few hashtags onto an Instagram photo, you'll know doing so isn't exactly a research-grade process. There is generally some sort of method to why users tag an image with a specific hashtag; the challenge for Facebook was sorting what was relevant across billions of images. When you're operating at this scale -- the largest of the tests used 3.5 billion Instagram images spanning 17,000 hashtags -- even Facebook doesn't have the resources to closely supervise the data.

We propose a simple, robust, and scalable reverse image search engine that leverages convolutional features from Keras' pre-trained neural networks and the distance metric from Scikit-Learn's K-Nearest Neighbors. We show example queries using data scraped from Google images, and dive deeper in how we use the search engine to track the proliferation of memes from the dark web.